Methods and apparatus for payment fraud detection

ABSTRACT

This application relates to apparatus and methods for identifying fraudulent transactions. In some examples, a computing device generates a decision matrix to identify fraudulent transactions. To generate the decision matrix, the computing device may determine scores for a plurality of transactions, and may determine transaction categories for each transaction based on the scores. The computing device may also determine a number of predictable features based on applying machine learning techniques to the transactions. A risk category is then determined for the number of predictable features. The computing device generates the decision matrix based on the transaction categories and the risk categories. In some examples, the computing device applies the generated decision matrix to an ongoing purchase transaction to determine if the ongoing purchase transaction is fraudulent. In some examples, the computing device prevents completion of the purchase transaction if the purchase transaction is determined to be fraudulent.

TECHNICAL FIELD

The disclosure relates generally to fraud detection and, morespecifically, to identifying fraudulent retail activities.

BACKGROUND

Some transactions, such as some in-store or online retail transactions,are fraudulent. For example, a customer may attempt to purchase an itemusing a payment form, such as a credit card, belonging to anotherperson. The customer may have stolen or found the payment form, and isnow attempting to use the payment form for the purchase withoutpermission from the payment form's rightful owner. In some cases, suchas with in-store purchases, a customer may present another'sidentification (ID) card (e.g., driver's license), in addition to thepayment form, when attempting to purchase the item, thereby facilitatingthe in-store fraudulent purchase.

Conveniences associated with online retail purchases also may facilitatefraudulent online transactions. For example, at least some retailwebsites allow a customer to make purchases without “signing in.”Instead of logging into an account of the customer on the website, thecustomer may choose to proceed under a “guest” option that does notrequire the customer to sign in to a particular account. In addition, atleast some retail websites allow a customer to ship purchased productsto any address, such as a store location (e.g., ship-to-store), or ahome location (e.g., ship-to-home). Although some retailers may requirethe showing of an ID when a customer shows to pick up a purchased itemat a store, as noted above the customer may have an ID card of thevictimized person. Thus, these online purchase conveniences mayfacilitate fraudulent online retail transactions.

In each of these examples, the customer is involved in a fraudulentactivity. Fraudulent activities may cause financial harm to a company,such as a retailer. For example, the true owner of the payment form mayidentify the fraudulent transaction and have the transaction cancelled.As such, the retailer may not receive payment for the purchase items.Thus, retailers may benefit from identifying fraudulent transactionsbefore they are completed.

SUMMARY

The embodiments described herein are directed to automaticallyidentifying fraudulent transactions. The embodiments may identify afraudulent activity as it is taking place, for example, allowing aretailer to stop or not allow the transaction. In some examples, theembodiments may allow a retailer to identify a suspected fraudulentin-store or online purchase. The transaction may be disallowed, or maybe presented for closer review to determine if fraud is indeed involved.As a result, the embodiments may allow a retailer to decrease expensesrelated to fraudulent transactions.

In accordance with various embodiments, exemplary systems may beimplemented in any suitable hardware or hardware and software, such asin any suitable computing device. For example, in some embodiments, acomputing device is configured to obtain a plurality of valuescorresponding to each of a plurality of transactions. The computingdevice is also configured to determine a value category for each of theplurality of transactions based on the plurality of values correspondingto each of the plurality of transactions. Further, the computing deviceis configured to determine a plurality of features based on theplurality of transactions and the plurality of values, where each of theplurality of features is associated with at least one value of theplurality of values corresponding to the plurality of transactions. Thecomputing device is further configured to determine a risk category foreach of the plurality of features based on the associated at least onevalue. The computing device is also configured to generate decision databased on the determined value categories and risk categories, where thedecision data identifies a plurality of conditions for determiningwhether a second transaction is fraudulent.

In some embodiments, a method is provided that includes obtaining aplurality of values corresponding to each of a plurality oftransactions. The method also includes determining a value category foreach of the plurality of transactions based on the plurality of valuescorresponding to each of the plurality of transactions. The methodfurther includes determining a plurality of features based on theplurality of transactions and the plurality of values, where each of theplurality of features is associated with at least one value of theplurality of values corresponding to the plurality of transactions.Further, the method includes determining a risk category for each of theplurality of features based on the associated at least one value. Themethod also includes generating decision data based on the determinedvalue categories and risk categories, where the decision data identifiesa plurality of conditions for determining whether a second transactionis fraudulent.

In yet other embodiments, a non-transitory computer readable medium hasinstructions stored thereon, where the instructions, when executed by atleast one processor, cause a computing device to perform operations thatinclude determining a value category for each of the plurality oftransactions based on the plurality of values corresponding to each ofthe plurality of transactions. The operations further includedetermining a plurality of features based on the plurality oftransactions and the plurality of values, where each of the plurality offeatures is associated with at least one value of the plurality ofvalues corresponding to the plurality of transactions. Further, theoperations include determining a risk category for each of the pluralityof features based on the associated at least one value. The operationsalso includes generating decision data based on the determined valuecategories and risk categories, where the decision data identifies aplurality of conditions for determining whether a second transaction isfraudulent.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of the present disclosures will be morefully disclosed in, or rendered obvious by the following detaileddescriptions of example embodiments. The detailed descriptions of theexample embodiments are to be considered together with the accompanyingdrawings wherein like numbers refer to like parts and further wherein:

FIG. 1 is a block diagram of a fraud detection system in accordance withsome embodiments;

FIG. 2 is a block diagram of the fraud detection computing device of thefraud detection system of FIG. 1 in accordance with some embodiments;

FIG. 3 is a block diagram illustrating examples of various portions ofthe fraud detection system of FIG. 1 in accordance with someembodiments;

FIG. 4 is a block diagram illustrating examples of various portions ofthe fraud detection computing device of FIG. 1 in accordance with someembodiments;

FIG. 5 is an example of a decision matrix that may be generated by thefraud detection computing device of FIG. 2 in accordance with someembodiments;

FIG. 6 is a flowchart of an example method that can be carried out bythe fraud detection system 100 of FIG. 1 in accordance with someembodiments;

FIG. 7 is a flowchart of another example method that can be carried outby the fraud detection system 100 of FIG. 1 in accordance with someembodiments;

FIG. 8 is a flowchart of yet another example method that can be carriedout by the fraud detection system 100 of FIG. 1 in accordance with someembodiments.

DETAILED DESCRIPTION

The description of the preferred embodiments is intended to be read inconnection with the accompanying drawings, which are to be consideredpart of the entire written description of these disclosures. While thepresent disclosure is susceptible to various modifications andalternative forms, specific embodiments are shown by way of example inthe drawings and will be described in detail herein. The objectives andadvantages of the claimed subject matter will become more apparent fromthe following detailed description of these exemplary embodiments inconnection with the accompanying drawings.

It should be understood, however, that the present disclosure is notintended to be limited to the particular forms disclosed. Rather, thepresent disclosure covers all modifications, equivalents, andalternatives that fall within the spirit and scope of these exemplaryembodiments. The terms “couple,” “coupled,” “operatively coupled,”“operatively connected,” and the like should be broadly understood torefer to connecting devices or components together either mechanically,electrically, wired, wirelessly, or otherwise, such that the connectionallows the pertinent devices or components to operate (e.g.,communicate) with each other as intended by virtue of that relationship.

Turning to the drawings, FIG. 1 illustrates a block diagram of a frauddetection system 100 that includes a fraud detection computing device102 (e.g., a server, such as an application server), a web server 104,workstation(s) 106, database 116, and multiple customer computingdevices 110, 112, 114 operatively coupled over network 118. Frauddetection computing device 102, workstation(s) 106, web server 104, andmultiple customer computing devices 110, 112, 114 can each be anysuitable computing device that includes any hardware or hardware andsoftware combination for processing and handling information. Inaddition, each can transmit data to, and receive data from,communication network 118.

For example, fraud detection computing device 102 can be a computer, aworkstation, a laptop, a server such as a cloud-based server, or anyother suitable device. Each of multiple customer computing devices 110,112, 114 can be a mobile device such as a cellular phone, a laptop, acomputer, a table, a personal assistant device, a voice assistantdevice, a digital assistant, or any other suitable device.

Additionally, each of fraud detection computing device 102, web server104, workstations 106, and multiple customer computing devices 110, 112,114 can include one or more processors, one or more field-programmablegate arrays (FPGAs), one or more application-specific integratedcircuits (ASICs), one or more state machines, digital circuitry, or anyother suitable circuitry.

Although FIG. 1 illustrates three customer computing devices 110, 112,114, fraud detection system 100 can include any number of customercomputing devices 110, 112, 114. Similarly, fraud detection system 100can include any number of workstation(s) 106, fraud detection computingdevices 102, web servers 104, and databases 116.

Workstation(s) 106 are operably coupled to communication network 118 viarouter (or switch) 108. Workstation(s) 106 and/or router 108 may belocated at a store 109, for example. Workstation(s) 106 can communicatewith fraud detection computing device 102 over communication network118. The workstation(s) 106 may send data to, and receive data from,fraud detection computing device 102. For example, the workstation(s)106 may transmit data related to a transaction, such as a purchasetransaction, to fraud detection computing device 102. In response, frauddetection computing device 102 may transmit an indication of whether thetransaction is to be allowed. Workstation(s) 106 may also communicatewith web server 104. For example, web server 104 may host one or moreweb pages, such as a retailer's website. Workstation(s) 106 may beoperable to access and program (e.g., configure) the webpages hosted byweb server 104.

Fraud detection computing device 102 is operable to communicate withdatabase 116 over communication network 118. For example, frauddetection computing device 102 can store data to, and read data from,database 116. Database 116 can be a remote storage device, such as acloud-based server, a memory device on another application server, anetworked computer, or any other suitable remote storage. Although shownremote to fraud detection computing device 102, in some examples,database 116 can be a local storage device, such as a hard drive, anon-volatile memory, or a USB stick.

Communication network 118 can be a WiFi® network, a cellular networksuch as a 3GPP® network, a Bluetooth® network, a satellite network, awireless local area network (LAN), a network utilizing radio-frequency(RF) communication protocols, a Near Field Communication (NFC) network,a wireless Metropolitan Area Network (MAN) connecting multiple wirelessLANs, a wide area network (WAN), or any other suitable network.Communication network 118 can provide access to, for example, theInternet.

First customer computing device 110, second customer computing device112, and N^(th) customer computing device 114 may communicate with webserver 104 over communication network 118. For example, web server 104may host one or more webpages of a website. Each of multiple computingdevices 110, 112, 114 may be operable to view, access, and interact withthe webpages hosted by web server 104. In some examples, web server 104hosts a web page for a retailer that allows for the purchase of items.For example, an operator of one of multiple computing devices 110, 112,114 may access the web page hosted by web server 104, add one or moreitems to an online shopping cart of the web page, and perform an onlinecheckout of the shopping cart to purchase the items. In some examples,web server 104 may transmit data that identifies the attempted purchasetransaction to fraud detection computing device 102. In response, frauddetection computing device 102 may transmit an indication of whether thetransaction is to be allowed.

Fraud detection system 100 may identify fraudulent transactions. Forexample, fraud detection system 100 may identify an attempted in-storepurchase of an item as fraudulent. Fraud detection system 100 may alsoidentify online purchases as fraudulent. In some examples, frauddetection system 100 may prohibit the completion of a transactiondetermined to be fraudulent.

To identify fraudulent transactions, fraud detection system 100 maygenerate decision matrix data identifying conditions (e.g.,requirements) that must be met for a transaction to be identified asfraudulent. For example, fraud detection system 100 may receivetransaction data identifying in-store or online (e.g., real-time)transaction, and determine if the transaction is fraudulent based on thegenerated decision matrix data. In some examples, if the transaction isidentified as fraudulent, fraud detection system 100 may deny thetransaction. The decision matrix data may also identify conditions that,when met, indicate that a transaction is not fraudulent. If frauddetection 100 determines, based on the generated matrix decision data,that a transaction is not fraudulent, fraud detection system 100 mayallow the transaction. In some examples, fraud detection system 100identifies a transaction as needing review. In these examples, frauddetection system 100 may transmit the transaction for review (e.g., tobe approved) by a retailer (e.g., a review manager, employee of theretailer, review team, etc.). Fraud detection system 100 may allow ordeny the transaction based on the review of the transaction.

Feature Selection

To generate decision matrix data, fraud detection system 100 may executeone or more fraud detection models that operate on historicaltransaction data to generate fraud detection scores. Historicaltransaction data may include, for example, data identifying previouscustomers (e.g., client identification (ID), email address, homeaddress, favored store location, phone number, etc.), data identifyingprevious in-store transactions (e.g., purchase dates, item IDs, itempurchase amounts, etc.), and data identifying previous online purchases(e.g., online IDs, purchase dates, item IDs, item purchase amounts,etc.). The generated fraud detection scores from each fraud detectionmodel may identify, for example, probabilities that each particularhistorical transaction is a fraud. Each fraud detection model may bebased on, for example, one or more decision trees, supervised machinelearning algorithms such as Logic Regression, Support Vector Machines,Random Forest, Gradient Boosting Machines (e.g., XGBoost), or any othersuitable fraud detection models. The machine learning algorithms may betrained, for example, on historical transaction data.

In some examples, the fraud detection models are based on one or moremachine learning algorithms, such as Logistics Regression, RandomForest, Gradient Boosting Machines, or any other suitable learningalgorithms. In some examples, the fraud detection models operate onhistorical data that has been segmented. For example, fraud detectionsystem 100 may segment the historical data based on one or more featuresof each historical transaction, such as whether a customer is attemptingto use a guest checkout feature for an online purchase, or is using anaccount the customer signed into.

Fraud detection system 100 may also apply one or more machine learningalgorithms, such as Logistics Regression, Random Forest, GradientBoosting Machines, or any other suitable learning algorithms, toidentify a number of features of the historical transaction data thatare most predictable of the fraud detection scores. For example, thenumber of most predictable features may be the ones that are mostcorrelated with the generated fraud detection scores. For example, frauddetection system 100 may execute the one or more machine learningalgorithms to identify those features (e.g., portions of historicaldata) that, if present, are associated with a probability that atransaction is fraudulent over a certain amount, such as 45%. Featuresmay include any data related to the transaction, any data related to thecustomer's historical purchase transactions, any data related to thecustomer, or any other suitable data as determined by the machinelearning algorithms. The machine learning algorithms may be trained, forexample, on historical transaction data.

Binning Data

Fraud detection system 100 may also bin (e.g., group, categorize) thehistorical transaction data associated with each of the identifiedpredictable features. For example, fraud detection system 100 mayexecute one or more binning algorithms to bin the historical transactiondata associated with each of the identified predictable features intorisk categories. The binning algorithms may include, for example,feature scaling and normalization, weight of the evidence, variations ofprincipal component analysis (PCA), or any other suitable binningalgorithm. As an example, assume the length of an email address for acustomer is determined to be a most predictable feature of fraudulentactivities. Fraud detection system 100 may execute a binning algorithmthat associates each email address for each customer of each historicaltransaction in one of a number of bins based on the length of each emailaddress.

For example, a historical transaction that includes an email addresseswith a character length of less than or equal to 17 may be associatedwith a first bin. Historical transactions that include an email addresswith a character length of less than or equal to 19, but greater than17, may be associated with a second bin. Similarly, historicaltransactions that include an email address with a character length ofless than or equal to 21, but greater than 19, may be associated with athird bin; historical transactions that include an email address with acharacter length of less than or equal to 23, but greater than 22, maybe associated with a fourth bin; historical transactions that include anemail address with a character length of less than or equal to 26, butgreater than 23, may be associated with a fifth bin; and historicaltransactions that include an email address with a character lengthgreater than 26 may be associated with a sixth bin. In addition,historical transactions associated with no email address may beassociated with a seventh bin.

Fraud Detection Model Score Tier Generation

Based on the fraud detection scores generated for the most predictablefeatures in the feature selection step above, fraud detection system 100tiers (e.g., categorizes) each of the corresponding historicaltransactions. For example, fraud detection scores within a first rangemay be associated with a first tier, fraud detection scores within asecond range may be associated with a second tier, fraud detectionscores within a third range may be associated with a third tier, andfraud detection scores within a fourth range may be associated with afourth tier.

As noted above, in some examples, the fraud detection scores generatedby the fraud detection models may identify fraudulent probabilities. Inone example, fraud detection scores for transactions identifying a fraudprobability of greater than or equal to 40% are associated with a firsttier. Fraud detection scores for transactions identifying a fraudprobability of greater than or equal to 20%, but less than 40%, areassociated with a second tier. Fraud detection scores for transactionsidentifying a fraud probability of greater than or equal to 5%, but lessthan 20%, are associated with a third tier. Fraud detection scores fortransactions identifying a fraud probability less than 5% are associatedwith a fourth tier. Although in this example four tiers are described,the number of tiers may be less than, or greater than, four (e.g., 2, 5,10, 16, etc.).

In some examples, fraud detection system 100 may combine (e.g.,consolidate) the score tiers from various fraud detection models togenerate common tiers that each of the corresponding historicaltransactions may be associated with. For example, although differentfraud detection models may have different score ranges, the score tiersfor each of the fraud detection models would be the same.

Decision Matrix Generation

Based on the tier each historical transaction is associated with, aswell as the bin each predictable feature is associated with, frauddetection system 100 generates a decision matrix that identifiestransaction allowability decisions. The decision matrix may identifyvarious conditions (e.g., requirements) for a transaction to beidentified as fraudulent and therefore denied.

For example, as a customer is attempting to purchase one or more itemsat store 109, workstation 106 may transmit in-store transaction dataidentifying the transaction to fraud detection computing device 102.Fraud detection computing device 102 may determine whether thetransaction is to be allowed based on applying one or more frauddetection models to the particular transaction to determine acorresponding tier. In some examples, a determination is made as to asegment that the transaction is associated with. Based on the determinedsegment, a particular fraud detection model is applied to thetransaction. Fraud detection computing device 102 may also determine abin for each predictable feature associated with the particulartransaction. Fraud detection computing device 102 may then apply thedecision matrix to the determined tier and bins for the particulartransaction to determine if the transaction is fraudulent. In someexamples, one or more of fraud detection computing device 102 andworkstation 106 prevent the transaction from occurring if thetransaction is identified as fraudulent.

Similarly, as a customer is attempting to purchase, via a customercomputing device 110, 112, 114, one or more items on a website hosted byweb server 104, web server 104 may transmit online transaction dataidentifying the transaction to fraud detection computing device 102.Fraud detection computing device 102 may determine whether thetransaction is to be allowed based on applying the decision matrix todetermined tier and bins for the particular online transaction. In someexamples, one or more of fraud detection computing device 102 and webserver 104 prevent the transaction from occurring if the transaction isidentified as fraudulent.

FIG. 2 illustrates the fraud detection computing device 102 of FIG. 1.Fraud detection computing device 102 can include one or more processors201, working memory 202, one or more input/output devices 203,instruction memory 207, a transceiver 204, one or more communicationports 207, and a display 206, all operatively coupled to one or moredata buses 208. Data buses 208 allow for communication among the variousdevices. Data buses 208 can include wired, or wireless, communicationchannels.

Processors 201 can include one or more distinct processors, each havingone or more cores. Each of the distinct processors can have the same ordifferent structure. Processors 201 can include one or more centralprocessing units (CPUs), one or more graphics processing units (GPUs),application specific integrated circuits (ASICs), digital signalprocessors (DSPs), and the like.

Processors 201 can be configured to perform a certain function oroperation by executing code, stored on instruction memory 207, embodyingthe function or operation. For example, processors 201 can be configuredto perform one or more of any function, method, or operation disclosedherein.

Instruction memory 207 can store instructions that can be accessed(e.g., read) and executed by processors 201. For example, instructionmemory 207 can be a non-transitory, computer-readable storage mediumsuch as a read-only memory (ROM), an electrically erasable programmableread-only memory (EEPROM), flash memory, a removable disk, CD-ROM, anynon-volatile memory, or any other suitable memory.

Processors 201 can store data to, and read data from, working memory202. For example, processors 201 can store a working set of instructionsto working memory 202, such as instructions loaded from instructionmemory 207. Processors 201 can also use working memory 202 to storedynamic data created during the operation of fraud detection computingdevice 102. Working memory 202 can be a random access memory (RAM) suchas a static random access memory (SRAM) or dynamic random access memory(DRAM), or any other suitable memory.

Input-output devices 203 can include any suitable device that allows fordata input or output. For example, input-output devices 203 can includeone or more of a keyboard, a touchpad, a mouse, a stylus, a touchscreen,a physical button, a speaker, a microphone, or any other suitable inputor output device.

Communication port(s) 207 can include, for example, a serial port suchas a universal asynchronous receiver/transmitter (UART) connection, aUniversal Serial Bus (USB) connection, or any other suitablecommunication port or connection. In some examples, communicationport(s) 207 allows for the programming of executable instructions ininstruction memory 207. In some examples, communication port(s) 207allow for the transfer (e.g., uploading or downloading) of data, such astransaction data.

Display 206 can display user interface 205. User interfaces 205 canenable user interaction with fraud detection computing device 102. Forexample, user interface 205 can be a user interface for an applicationof a retailer that allows a customer to purchase one or more items fromthe retailer. In some examples, a user can interact with user interface205 by engaging input-output devices 203. In some examples, display 206can be a touchscreen, where user interface 205 is displayed on thetouchscreen.

Transceiver 204 allows for communication with a network, such as thecommunication network 118 of FIG. 1. For example, if communicationnetwork 118 of FIG. 1 is a cellular network, transceiver 204 isconfigured to allow communications with the cellular network. In someexamples, transceiver 204 is selected based on the type of communicationnetwork 118 fraud detection computing device 102 will be operating in.Processor(s) 201 is operable to receive data from, or send data to, anetwork, such as communication network 118 of FIG. 1, via transceiver204.

FIG. 3 is a block diagram illustrating examples of various portions ofthe fraud detection system of FIG. 1. In this example, fraud detectioncomputing device 102 can receive from a store 109 (e.g., from acomputing device, such as workstation 106, at store location 109) storepurchase data 302 identifying the purchase attempt of one or more items.Store purchase data 302 may include, for example, one or more of thefollowing: an identification of one or more items being purchased; anidentification of the customer (e.g., customer ID, passport ID, driver'slicense number, etc.); an image of an identification of the customer; amonetary amount (e.g., price) of each item being returned; the method ofpayment used to purchase the items (e.g., credit card, cash, check); aUniversal Product Code (UPC) number for each item; a time and/or date;and/or any other data related to the attempted purchase transaction.

Fraud detection computing device 102 may execute one or more frauddetection models based on store purchase data 302 to generate one ormore fraud detection scores. In some examples, fraud detection score 102associates customer history data 350 with store purchase data 302. Forexample, store purchase data 302 may identify a name or ID of thecustomer. Based on the name or ID of the customer, fraud detectioncomputing device may obtain customer history data 350 from database 116for the customer. Customer history data 350 may include, for example, acustomer ID 352 (e.g., a customer name, an ID number, online ID, etc.),store history data 354 identifying historical in-store purchasetransactions, and online history data 356 identifying online purchasetransactions for the customer.

Fraud detection computing device 102 may determine a tier based on thefraud detection scores. For example, fraud detection computing device102 may determine the tier based on a range each fraud detection scorefalls within. Fraud detection computing device 102 may then consolidatethe determined tiers to generate a global tier for the purchasetransaction identified by store purchase data 302.

Fraud detection computing device 102 may also determine a bin for one ormore predictable features associated with store purchase data 302. Thepredictable features may have been predetermined by fraud detectioncomputing device. For example, fraud detection computing device 102 maydetermine, based on store purchase data 302 and/or associated customerhistory data 350, a bin for each predetermined predictable feature basedon the application of one or more binning algorithms. In some examples,the binning algorithm is a decision tree. Execution of the decision treemay determine a bin based on a value of the predictable feature. In someexamples, execution of the decision tree may determine a bin based onthe presence, or absence, of the predictable feature. For example, thedecision tree may determine a first bin if the customer has made apurchase in the last 30 days, and determine a second bin if not.

Fraud detection computing device 102 may then apply a decision matrix,such as one identified by decision matrix data 370 in database 116, tothe determined tier and bins to determine if the transaction identifiedby store purchase data 302 is fraudulent. For example, fraud detectioncomputing device 102 may compare the determined tier and bins to tiersand bins identified by decision matrix data 370 to determine whetherstore purchase data 302 is fraudulent.

Similarly, fraud detection computing device 102 can receive from a webserver 104 online purchase data 310 identifying the purchase attempt ofone or more items online, such as from a website hosted by web server104. For example, a customer may engage customer computing device 112 toaccess the website hosted by web server 104, which may be a retailer'swebsite that allows for the purchase of one or more items. Upon inputfrom the customer, customer computing device 112 may transmit purchaserequest data 306 to web server 104. Purchase request data 306 mayidentify a purchase request of one or more items, such as one or moreitems the customer has added to an online shopping cart of the website.In response, web server 104 transmits online purchase data 310 to frauddetection computing device 102.

Online purchase data 310 may include, for example, one or more of thefollowing: an identification of one or more items being purchased; anidentification of the customer (e.g., customer ID, passport ID, driver'slicense number, etc.); an image of an identification of the customer; amonetary amount (e.g., price) of each item being returned; the method ofpayment used to purchase the items (e.g., credit card, cash, check); aUniversal Product Code (UPC) number for each item; a time and/or date;whether the customer is attempting a “guest” checkout or a “signed-in”checkout; online account information for the customer; and/or any otherdata related to the attempted purchase transaction.

Fraud detection computing device 102 may execute one or more frauddetection models based on online purchase data 310 to generate one ormore fraud detection scores. In some examples, fraud detection score 102associates customer history data 350 with store purchase data 302. Forexample, online purchase data 310 may identify a name or online ID ofthe customer. Based on the name or online ID of the customer, frauddetection computing device may obtain customer history data 350 fromdatabase 116 for the customer. Customer history data 350 may include,for example, a customer ID 352 (e.g., a customer name, an ID number,online ID, etc.), store history data 354 identifying historical in-storepurchase transactions, and online history data 356 identifying onlinepurchase transactions for the customer.

Fraud detection computing device 102 may determine a tier based on thefraud detection scores. For example, fraud detection computing device102 may determine the tier based on a range each fraud detection scorefalls within. Fraud detection computing device 102 may then consolidatethe determined tiers to generate a global tier for the purchasetransaction identified by store purchase data 302.

Fraud detection computing device 102 may also determine a bin for one ormore predictable features associated with online purchase data 310. Thepredictable features may have been predetermined by fraud detectioncomputing device. For example, fraud detection computing device 102 maydetermine, based on online purchase data 310 and/or associated customerhistory data 350, a bin for each predetermined predictable feature basedon the application of one or more binning algorithms. In some examples,the binning algorithm is a decision tree. Execution of the decision treemay determine a bin based on a value of the predictable feature. In someexamples, execution of the decision tree may determine a bin based onthe presence, or absence, of the predictable feature. For example, thedecision tree may determine a first bin if the customer has made apurchase in the last 30 days, and determine a second bin if not.

Fraud detection computing device 102 may then apply a decision matrix,such as one identified by decision matrix data 370 in database 116, tothe determined tier and bins to determine if the transaction identifiedby online purchase data 310 is fraudulent. For example, fraud detectioncomputing device 102 may compare the determined tier and bins to tiersand bins identified by decision matrix data 370 to determine whetheronline purchase data 310 is fraudulent.

FIG. 5 illustrates an example decision matrix 500 that may be identifiedand characterized by decision matrix data 370. As illustrated, decisionmatrix 500 includes a plurality of columns and a plurality of rows.Specifically, decision matrix 500 includes a fraud probability tiercolumn 502 identifying fraud detection model tiers, which may have beenpreviously determined. For example, fraud probability tier column 502identifies tiers “B,” (in rows 550, 552, 554, and 556) “C,” (in row 558)and “D” (in row 560). Decision matrix 500 also includes multiplepredictable feature columns 504, 506, 508, where each predictablefeature column corresponds to a predetermined predictable feature.Moreover, each predictable feature column identifies a bin (e.g., a riskcategory) for the corresponding predictable feature. Each bin may havebeen previously determined. Specifically, in this example, decisionmatrix 500 includes first feature bin column 504, second feature bincolumn 506, and last feature bin column 508. Based on the number ofpredictable features, decision matrix 500 may include less, or more,predictable feature columns, where each predictable feature columncorresponds to a previously determined predictable feature. Decisionmatrix 500 may include a row for any possible combination of fraudprobability tiers and predictable features that a transaction, such as areal-time transaction, may be associated with.

Decision matrix 500 also includes a fraud rate column 510, an impactedorders column 512, and an impacted gross merchandise volume (GMV) column514. Fraud rate column 510 identifies a historical fraud rate based onhistorical transaction data identifying transactions over a period oftime (e.g., week, month, year, etc.) that meet the conditions identifiedin the corresponding fraudulent probability tier column 502 and multiplepredictable feature columns 504, 506, 508. For example, out of alltransactions over the period of time that meet the conditions of row 550in the corresponding fraudulent probability tier column 502 and multiplepredictable feature columns 504, 506, 508, 45% of those transactionswere fraudulent.

Impacted orders column 512 identifies a percentage of all historicalorders over a period of time that were fraudulent and met the conditionsidentified in the corresponding fraudulent probability tier column 502and multiple predictable feature columns 504, 506, 508. For example,impacted orders column 512 identifies in row 550 that 0.5% of all ordersover a period of time were deemed fraudulent (e.g., “impacted”) inaccordance with the corresponding fraudulent probability tier column 502and multiple predictable feature columns 504, 506, 508.

The impacted GMV column 514 identifies a percentage of merchandiseassociated with all historical orders over a period of time that werefraudulent and met the conditions identified in the correspondingfraudulent probability tier column 502 and multiple predictable featurecolumns 504, 506, 508. For example, impacted GMV column 514 identifiesin row 550 that 0.3% of all gross merchandise associated with historicalorders over a period of time were deemed fraudulent (e.g., “impacted”)in accordance with the corresponding fraudulent probability tier column502 and multiple predictable feature columns 504, 506, 508.

Decision matrix 500 also includes a decision column 560 that identifiesa decision of whether a transaction is fraudulent. For example,decisions may include “ALLOW,” where the transaction is allowed, “DENY,”where the transaction is denied, or “CHALLENGE,” where the transactionis challenged, as described in further detail below. The decisions aregenerated based on the fraud rate column 510, the impacted order column512, and the impacted GMV column 514 corresponding to the same row asthe decision. In some examples, the decisions are configured by a userbased on the corresponding values of the fraud rate column 510, theimpacted order column 512, and the impacted GMV column 514. For atransaction meeting all of the conditions listed in a particular row(e.g., row 550) including the fraudulent probability tier column 502 andmultiple predictable feature columns 504, 506, 508, the decision in thecorresponding decision column 560 is generated.

For example, if a transaction, such as one identified by store purchasedata 302 or online purchase data 310, satisfies the conditions locatedin row 550 including a fraud probability tier of “B,” a firstpredictable feature bin of “R1,” a second predictable feature bin of“R1,” and a last predictable feature bin of “R1,” the decision matrixidentifies a decision of “DENY.” In this example, fraud detectioncomputing device 102 determines that the transaction is fraudulent andshould be denied. If, however, a transaction satisfies the conditions inrow 560, including a fraud probability tier of “D,” a first predictablefeature bin of “R4,” a second predictable feature bin of “R3,” and alast predictable feature bin of “R4,” the decision matrix identifies adecision of “ACCEPT.” In this example, fraud detection computing device102 determines that the transaction is not fraudulent and should beallowed.

As a third example, if a transaction satisfies the conditions located inrow 558 including a fraud probability tier of “C,” a first predictablefeature bin of “R1,” a second predictable feature bin of “R3,” and alast predictable feature bin of “R3,” the decision matrix identifies adecision of “CHALLENGE.” Here, fraud detection computing device 102determines that the transaction should be further reviewed, such as by areviewer, and thus should be forwarded for such review to determine ifthe transaction should be allowed.

For example, and with reference back to FIG. 3, if fraud detectioncomputing device 102 determines, based on decision matrix data 370(e.g., decision matrix 500), that a transaction should be challenged,fraud detection computing device 102 transmits review request data 319to review center 320. Review center 320 may include, for example, one ormore computing devices, such as servers, to receive review request data319. Review request data 319 identifies and characterizes thetransaction identified by either store purchase data 302 or onlinepurchase data 310 (based on which was received), and may include anidentification of associated customer history data 350. In someexamples, at review center 320, the one or more computing devicesdisplay at least a portion of review request data 319, such as via adisplay, for review by one or more review personnel. In some examples,review personnel may request additional data regarding the transaction,such as associated customer history data 350, from database 116. Oncereview personnel make a decision, they may provide an input, such as viaa keyboard or a touchscreen, either allowing, or denying, thecorresponding transaction. In response, the one or more computingdevices transmit review response data 321 to fraud detection computingdevice 102, where review response data 321 identifies whether thecorresponding transaction is to be allowed. For example, review responsedata 321 may identify whether to “ACCEPT” or “DENY” the transaction.

As illustrated in FIG. 3, decision matrix data 370 may identify aplurality of decision matrices, such first decision matrix 372 up tolast decision matrix 374. In some examples, a plurality of decisionmatrices may include differing transaction allowability decisions (e.g.,as indicated in decision column 510 of decision matrix 500) for similarfraudulent probability tier and predictable feature conditions. Forexample, while first decision matrix 372 may include the same conditionsand decision outcomes as illustrated in decision matrix 500 of FIG. 5,last decision matrix 374 may include differing decision outcomes for thesame conditions. As an example, row 556 of decision matrix 500 indicatesin decision column 510 a decision of “DENY.” Given the same conditions,last decision matrix 374 may indicate a decision of “ALLOW” for row 556.

In some examples, fraud detection computing device 102 may select whichof the plurality of decision matrixes to apply to a given transaction(e.g., as identified by store purchase data 302 and online purchase data310). In some examples, a user may provide a configuration settingindicating to fraud detection computing device 102 which of theplurality of decision matrices to apply. In some examples, frauddetection computing device 102 determines which of the plurality ofdecision matrices to apply to a transaction based on one or more of atime of year, geographic location of the purchasing customer, volume ofmerchandise being sold in a period (e.g., volume that has sold over lastthree months), or any other suitable consideration.

Once fraud detection computing device 102 determines whether atransaction, such as one identified by store purchase data 302 or onlinepurchase data 310, is fraudulent by applying a decision matrixidentified by decision matrix data 370, fraud detection computing device102 may generate a response indicating whether the transaction isallowed. For example, after determining whether a transaction identifiedby store purchase data 302 is allowed, fraud detection computing device102 may generate store allowance data 304 identifying whether thetransaction is allowed (e.g., “ALLOWED” or “DENIED”). Fraud detectioncomputing device 102 may transmit store allowance data 304 to store 109.If fraud detection computing device 102 determined that the transactionneeds further review (e.g., “CHALLENGE”) (and transmitted review requestdata 319 to review center 320), upon receiving the decision identifiedin review response data 321, fraud detection computing device 102generates and transmits store allowance data 304 identifying thedecision received to store 109.

Similarly, after determining whether a transaction identified by onlinepurchase data 310 is allowed, fraud detection computing device 102 maygenerate online allowance data 312 identifying whether the transactionis allowed (e.g., “ALLOWED” or “DENIED”). Fraud detection computingdevice 102 may transmit online allowance data 312 to web server 104. Iffraud detection computing device 102 determined that the transactionneeded further review (e.g., “CHALLENGE”) (and transmitted reviewrequest data 319 to review center 320), upon receiving the decisionidentified in review response data 321, fraud detection computing device102 generates and transmits online allowance data 312 identifying thedecision received to web server 104.

Web server 104 may then generate and transmit to customer computingdevice 112 purchase response data 308 identifying the decision (e.g.,“PURCHASE COMPLETE” for allowed transactions, or “PURCHASE DENIED” fordenied transactions). In some examples customer computing device 112 maydisplay the decision.

FIG. 4 is a block diagram illustrating examples of various portions ofthe fraud detection computing device 102 of FIG. 1. As indicated in thefigure, fraud detection computing device 102 includes fraud probabilitydetermination engine 402, feature determination engine 404, bindetermination engine 406, tier determination and consolidation engine408, and decision matrix generation engine 410. In some examples, one ormore of fraud probability determination engine 402, featuredetermination engine 404, bin determination engine 406, tierdetermination and consolidation engine 408, and decision matrixgeneration engine 410 may be implemented in hardware. In some examples,one or more of fraud probability determination engine 402, featuredetermination engine 404, bin determination engine 406, tierdetermination and consolidation engine 408, and decision matrixgeneration engine 410 may be implemented as an executable programmaintained in a tangible, non-transitory memory, such as instructionmemory 207 of FIG. 2, that may be executed by one or processors, such asprocessor 201 of FIG. 2.

Fraud probability determination engine 402 may execute one or more frauddetection models (e.g., algorithms), such a model identified andcharacterized by fraud probability determination algorithm data 422, togenerate fraud detection scores. The fraud detection models may operateon customer history data 350, and may generate fraud detection scoresthat identify probabilities that each transaction identified by customerhistory data 350 is a fraud. Fraud probability determination algorithmdata 422 may identify and characterize, for example, one or moredecision trees, supervised machine learning algorithms, or any othersuitable fraud detection models. Fraud probability determination engine402 may generate fraud probability data 417 identifying the frauddetection scores.

Feature determination engine 404 receives fraud probability data 417from fraud probability determination engine 402 and, based on theidentified fraud detection scores, applies one or more machine learningalgorithms, such as Logistics Regression, Random Forest, GradientBoosting Machines, or any other suitable learning algorithms, toidentify a number of most predictable features identified by customerhistory data 350. For example, feature determination engine 404 mayexecute one or more algorithms identified by fraud probabilitydetermination algorithm data 422 to determine those features in customerhistory data 350 that, if present, are associated with a probabilitythat a transaction is fraudulent. In some examples, a user may configurethe number of most predictable features. For example, the user mayprovide an input to fraud detection computing device 102 via, forexample, input/output device 203, identifying the number of mostpredictable features. In this example, feature determination engine 404determines the selected number of features that are most predictable.Feature determination engine 404 generates feature data 414 identifyingand characterizing the determined predictable features.

Bin determination engine 406 receives feature data 414 and bins eachidentified predictable feature into one or more bins (e.g., categories).For example, bin determination engine 406 may obtain ginning algorithmdata 426 from database 116, which may identify and characterize one ormore binning algorithms. The binning algorithms may include, forexample, feature scaling and normalization, weight of the evidence,variations of principal component analysis (PCA), or any other suitablebinning algorithm. Bin determination engine 406 generates binned featuredata 416 identifying the bin associated with each predictable feature.

Tier determination and consolidation engine 408 determines a tier for aplurality of transactions identified by customer history data 350 basedon fraud probability data 417 obtained from fraud probabilitydetermination engine 402. For example, depending on the fraud detectionscore associated with each transaction for each model (e.g., modelsexecuted by fraud probability determination engine 402), the transactionis associated with a tier. For example, transactions that, according toa first model, result in a fraudulent detection score that falls withina first range may be associated with a first tier. Similarly, frauddetection scores within a second range may be associated with a secondtier, fraud detection scores within a third range may be associated witha third tier, and fraud detection scores within a fourth range may beassociated with a fourth tier, for each model. Tier determination andconsolidation engine 408 may then consolidate the tiers from variousfraud detection models to generate common tiers that each of thecorresponding historical transactions (e.g., as identified by customerhistory data 350) may be associated with. Tier determination andconsolidation engine 408 generates tier-based fraud probabilityconsolidation data 418 identifying and characterizing the common tiersassociated with each of the historical transactions.

Decision matrix generation engine 410 generates decision matrix data 370identifying one or more decision matrices based on tier-based fraudprobability consolidation data 418 and binned feature data 416. Decisionmatrix data 370 identifies transaction allowability decisions based onone or more of the common tiers identified by tier-based fraudprobability consolidation data 418 and the bins identified by binnedfeature data 416. Decision matrix generation engine 410 may storedecision matrix data 370 in database 116, for example. Fraud detectioncomputing device 102 may employ one or more decision matrices identifiedand characterized by decision matrix data 370 when receiving indicationof a transaction, such as a real-time transaction (e.g., a transactionidentified by store purchase data 302 and/or online purchase data 310).

FIG. 6 is a flowchart of an example method 600 that can be carried outby the fraud detection system 100 of FIG. 1. Beginning at step 602, foreach of a plurality of transactions, a fraud detection score is obtainedfrom each of a plurality of fraud detection models. For example, frauddetection computing device 102 may execute a plurality of frauddetection models that operate on historical transaction data (e.g.,consumer history data 350) to generate the plurality of fraud detectionscores, where each fraud detection score is associated with atransaction identified by the historical transaction data. At step 604,a score category for each of the plurality of transactions is determinedbased on each transaction's corresponding fraud detection scores. Forexample, fraud detection computing device 102 may determine a tier foreach transaction based on the fraud detection score determined by eachmodel.

Proceeding to step 606, a plurality of most predictable features aredetermined. The most predictable features are determined based onapplying one or more machine learning algorithms to the plurality oftransactions. For example, fraud detection computing device 102 mayprovide the plurality of transactions to a machine learning algorithm,where each transaction is associated with one or more fraud detectionscores (e.g., supervised learning). Execution of the machine learningalgorithm may yield a number of predictable features (e.g., a number offeatures associated with fraud detection scores of 45% or greater).

At step 608, a risk category is determined for each predictable feature.The risk categories may be determined based on applying one or morebinning algorithms to the predictable features. For example, frauddetection computing device 102 may apply one or more binning algorithmsas identified by binning algorithm data 426 to the predictable features.At step 610, a decision matrix is generated based on the scorecategories for the plurality of transactions and on the risk categoriesfor the predictable features. The decision matrix identifies transactionallowability decisions (e.g., such as “ALLOW,” “DENY,” or “CHALLENGE”).For example, fraud detection computing device 102 may generate decisionmatrix data 370 identifying and characterizing one or more decisionmatrices. In some examples, the decision matrix is stored in memory,such as in database 116. The method then ends.

FIG. 7 is a flowchart of another example method 700 that can be carriedout by the fraud detection system 100 of FIG. 1. At step 702, purchasedata identifying and characterizing a purchase transaction is received.For example, fraud detection computing device 102 may receive onlinepurchase data 312 identifying an attempted online purchase transaction.At step 704, a fraud detection score for each of a plurality of frauddetection models is determined based on the received purchase data. Forexample, fraud detection computing device 102 may apply a plurality offraud detection models to online purchase data 312 to determine a frauddetection score for each model.

Proceeding to step 706, a score category for the purchase transaction isdetermined based on the determined fraud detection scores. As anexample, fraud detection computing device 102 may determine a tier basedon a score range each fraud detection score falls within. Frauddetection computing device 102 may then consolidate the determined tiersto generate the score category (e.g., global tier) for the purchasetransaction. At step 708, a risk category is determined for each of aplurality of predictable features based on the received purchase data.For example, fraud detection computing device 102 may determine whetherthe received purchase data includes each predictable feature, anddetermine the risk category for the predictable feature based on thedetermination (e.g., one value if the predictable feature is included,another value if the predictable feature is not included).

At step 710, based on the determined fraud detection scores and the riskcategories, a transaction allowability decision is determined. Forexample, fraud detection computing device 102 may apply a decisionmatrix, such as the one generated by method 600, to determine whetherthe purchase transaction should be allowed, denied, or challenged. Insome examples, fraud detection computing device 102 compares thedetermined fraud detection scores and risk categories correspond toconditions identified in the decision matrix that result in a denial ofthe purchase transaction. If the conditions that result in a denial aresatisfied by the determined fraud detection scores and risk categories,fraud detection computing device 102 disallows the transaction (e.g.,does not allow the transaction to complete).

Proceeding to step 712, a determination is made as to whether thetransaction allowability decision is a challenge. If the transactionallowability decision is a challenge, the method proceeds to step 714,where the purchase data is transmitted to a review team, such as atreview center 320, for a determination as to whether the transactionshould be allowed. For example, fraud detection computing device 102 maytransmit review request data 319 identifying the transaction to reviewcenter 320. From step 714 the method proceeds to step 716, where anallowability decision is received from the review team. For example,fraud detection computing device 102 may receive review response data321 identifying whether the transaction should be allowed (e.g., “ALLOW”or “DENY”).

From step 716 the method proceeds to step 718. In addition, if, back atstep 712, the transaction allowability decision is not a challenge, themethod also proceeds to step 718. At step 718, the transactionallowability decision is transmitted in response to the receivedpurchase data. For example, fraud detection computing device 102 maytransmit purchase allowance data 312 to web server 104 identifyingwhether the transaction identified by online purchase data 310 should beallowed. The method then ends.

FIG. 8 is a flowchart of another example method 800 that can be carriedout by the fraud detection system 100 of FIG. 1. At step 802, historicaltransaction data, such as historical transaction data identified bycustomer history data 350, is obtained. At step 804, a machine learningalgorithm, such as one based on Gradient Boosting Machines or LogicRegression, is applied to the historical transition data to determinefeatures. Proceeding to step 806, the most predictable features of thefeatures identified in step 804 are identified. For example, the mostpredictable features may be those with higher relative scores asgenerated by the machine learning algorithm. At step 808, the mostpredictable features are binned based on applying a binning model to themost predictable features.

Back at step 802, the method, either simultaneously or subsequently toproceeding to step 804, also proceeds to step 810. At step 810, frauddetection scores are generated based on applying a machine learningmodel, such as one based on Gradient Boosting Machines or LogicRegression, to the historical transaction data. Proceeding to step 812,a score tier is determined for each fraud detection score.

From steps 808 and 812, the method proceeds to step 814 where a decisionmatrix, such as one identified by decision matrix data 370, isgenerated. The decision matrix is generated based on the binned featuresand the score tiers. The method then ends.

Although the methods described above are with reference to theillustrated flowcharts, it will be appreciated that many other ways ofperforming the acts associated with the methods can be used. Forexample, the order of some operations may be changed, and some of theoperations described may be optional.

In addition, the methods and system described herein can be at leastpartially embodied in the form of computer-implemented processes andapparatus for practicing those processes. The disclosed methods may alsobe at least partially embodied in the form of tangible, non-transitorymachine-readable storage media encoded with computer program code. Forexample, the steps of the methods can be embodied in hardware, inexecutable instructions executed by a processor (e.g., software), or acombination of the two. The media may include, for example, RAMs, ROMs,CD-ROMs, DVD-ROMs, BD-ROMs, hard disk drives, flash memories, or anyother non-transitory machine-readable storage medium. When the computerprogram code is loaded into and executed by a computer, the computerbecomes an apparatus for practicing the method. The methods may also beat least partially embodied in the form of a computer into whichcomputer program code is loaded or executed, such that, the computerbecomes a special purpose computer for practicing the methods. Whenimplemented on a general-purpose processor, the computer program codesegments configure the processor to create specific logic circuits. Themethods may alternatively be at least partially embodied in applicationspecific integrated circuits for performing the methods.

The foregoing is provided for purposes of illustrating, explaining, anddescribing embodiments of these disclosures. Modifications andadaptations to these embodiments will be apparent to those skilled inthe art and may be made without departing from the scope or spirit ofthese disclosures.

What is claimed is:
 1. A system comprising: a computing device, whereinthe computing device comprises a fraud probability determination engine,a tier determination and consolidation engine, a feature determinationengine, a bin determination engine and a decision matrix generationengine, wherein: the computing device is configured to: apply, via thefraud probability determine engine, a first machine learning process toeach of a plurality of transactions, wherein the applying the firstmachine learning process further comprises generating a plurality ofvalues, wherein each of the plurality of values comprises a probabilitythat a corresponding transaction of the plurality of transactions isfraudulent; determine, via the tier determination and consolidationengine, a value category for each of the plurality of transactions basedon the plurality of values corresponding to each of the plurality oftransactions; determine, via the feature determination engine, aplurality of features that are predictable of the plurality of valuesbased on applying a second machine learning process to the plurality oftransactions and the probabilities of the plurality of values, whereineach of the plurality of features is associated with at least one valueof the plurality of values corresponding to the plurality oftransactions; determine, via the bin determination engine, a riskcategory for each of the plurality of features based on applying atleast one binning process to the associated at least one value; andgenerate, via the decision matrix generation engine, decision data basedon the determined value categories of the plurality of transactions andthe determined risk categories of the plurality of features, wherein thedecision data comprises a plurality of conditions for determiningwhether a transaction is fraudulent; wherein the computing device isfurther configured to: receive a purchase transaction; determine whetherthe purchase transaction is fraudulent by applying the generateddecision data; generate allowance data for the purchase transactionbased on determining that the purchase transaction is not fraudulent;and transmit the allowance data in response to the received purchasetransaction.
 2. The system of claim 1, wherein the plurality ofconditions comprises: at least one condition that the transaction isassociated with at least one value category; and at least one conditionthat the transaction is associated with at least one risk category. 3.The system of claim 1, wherein determining whether the transaction isfraudulent comprises determining whether the transaction is to beallowed or denied.
 4. The system of claim 3, wherein determining whetherthe transaction is fraudulent comprises determining whether thetransaction is to be reviewed.
 5. The system of claim 1, whereinapplying the first machine learning process to each of the plurality oftransactions comprises: applying each of a plurality of models to eachof the plurality of transactions, wherein the applying each of theplurality of models further comprises generating a model score for eachof the plurality of models; determining a model category for each of theplurality of models based on the generated model score; and determiningthe value category for each of the plurality of transactions based onthe determined model categories.
 6. The system of claim 1, wherein theplurality of transactions comprises historical purchase transactions. 7.The system of claim 1, wherein determining the plurality of featurescomprises determining a number of predictable features that are mostcorrelated with the plurality of values.
 8. The system of claim 1,wherein the computing device is configured to determine customer historydata associated with each of the plurality of transactions, whereindetermining the value category for each of the plurality of transactionsis based on the associated customer history data.
 9. A method using acomputing device comprising a fraud probability determination engine, atier determination and consolidation engine, a feature determinationengine, a bin determination engine and a decision matrix generationengine, the method comprising: applying, by the computing device via thefraud probability determine engine, a first machine learning process toeach of a plurality of transactions, wherein the applying the firstmachine learning process further comprises generating a plurality ofvalues, wherein each of the plurality of values comprises a probabilitythat a corresponding transaction of the plurality of transactions isfraudulent; determining, by the computing device via the tierdetermination and consolidation engine, a value category for each of theplurality of transactions based on the plurality of values correspondingto each of the plurality of transactions; determining, by the computingdevice via the feature determination engine, a plurality of featuresthat are predictable of the plurality of values based on applying asecond machine learning process to the plurality of transactions and theprobabilities of the plurality of values, wherein each of the pluralityof features is associated with at least one value of the plurality ofvalues corresponding to the plurality of transactions; determining, bythe computing device via the bin determination engine, a risk categoryfor each of the plurality of features based on applying at least onebinning process to the associated at least one value; and generating, bythe computing device via the decision matrix generation engine, decisiondata based on the determined value categories of the plurality oftransactions and the determined risk categories of the plurality offeatures, wherein the decision data comprises a plurality of conditionsfor determining whether a transaction is fraudulent; receiving, by thecomputing device, a purchase transaction; determining, by the computingdevice, whether the purchase transaction is fraudulent by applying thegenerated decision data; generating, by the computing device, allowancedata for the purchase transaction based on determining that the purchasetransaction is not fraudulent; and transmitting, by the computingdevice, the allowance data in response to the received purchasetransaction.
 10. The method of claim 9 wherein the plurality ofconditions comprises: at least one condition that the transaction isassociated with at least one value category; and at least one conditionthat the transaction is associated with at least one risk category. 11.The method of claim 9 wherein determining whether the transaction isfraudulent comprises determining whether the transaction is to beallowed or denied.
 12. The method of claim 9 wherein applying the firstmachine learning process to each of the plurality of transactionscomprises: applying each of a plurality of models to each of theplurality of transactions to generate a model score for each of theplurality of models; determining a model category for each of theplurality of models based on the generated model score; and determiningthe value category for each of the plurality of transactions based onthe determined model categories.
 13. A non-transitory computer readablemedium having instructions stored thereon, wherein the instructions,when executed by at least one processor of a computing device comprisinga fraud probability determination engine, a tier determination andconsolidation engine, a feature determination engine, a bindetermination engine and a decision matrix generation engine, cause thecomputing device to perform operations comprising: applying, via thefraud probability determine engine, a first machine learning process toeach of a plurality of transactions, wherein the applying the firstmachine learning process further comprises generating a plurality ofvalues, wherein each of the plurality of values comprises a probabilitythat a corresponding transaction of the plurality of transactions isfraudulent; determining, via the tier determination and consolidationengine, a value category for each of the plurality of transactions basedon the plurality of values corresponding to each of the plurality oftransactions; determining, via the feature determination engine, aplurality of features that are predictable of the plurality of valuesbased on applying a second machine learning process to the plurality oftransactions and the probabilities of the plurality of values, whereineach of the plurality of features is associated with at least one valueof the plurality of values corresponding to the plurality oftransactions; determining, via the bin determination engine, a riskcategory for each of the plurality of features based on applying atleast one binning process to the associated at least one value; andgenerating, via the decision matrix generation engine, decision databased on the determined value categories of the plurality oftransactions and the determined risk categories of the plurality offeatures, wherein the decision data comprises a plurality of conditionsfor determining whether a transaction is fraudulent; receiving apurchase transaction; determining whether the purchase transaction isfraudulent by applying the generated decision data; generating allowancedata for the purchase transaction based on determining that the purchasetransaction is not fraudulent; and transmitting the allowance data inresponse to the received purchase transaction.
 14. The non-transitorycomputer readable medium of claim 13 further comprising instructionsstored thereon that, when executed by the at least one processor,further cause the computing device to perform operations comprisingdetermining whether the transaction is to be allowed or denied.
 15. Thesystem of claim 1, wherein determining the value category for each ofthe plurality of transactions based on the plurality of valuescorresponding to each of the plurality of transactions comprises:determining a first value category for a transaction of the plurality oftransactions when the corresponding value is within a first range, ordetermining a second value category for the transaction when thecorresponding value is within a second range.
 16. The method of claim 9,wherein determining the value category for each of the plurality oftransactions based on the plurality of values corresponding to each ofthe plurality of transactions comprises: determining a first valuecategory for a transaction of the plurality of transactions when thecorresponding value is within a first range, or determining a secondvalue category for the transaction when the corresponding value iswithin a second range.
 17. The non-transitory computer readable mediumof claim 13 further comprising instructions stored thereon that, whenexecuted by the at least one processor, further cause the computingdevice to perform operations comprising: determining a first valuecategory for a transaction of the plurality of transactions when thecorresponding value is within a first range, or determining a secondvalue category for the transaction when the corresponding value iswithin a second range.